A Study on Deep Learning Methods in the Concept of Digital Industry 4.0

Mehmet Şimşek, Zeynep Orman
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Abstract

Nowadays, the main features of Industry 4.0 are interpreted to the ability of machines to communicate with each other and with a system, increasing the production efficiency and development of the decision-making mechanisms of robots. In these cases, new analytical algorithms of Industry 4.0 are needed. By using deep learning technologies, various industrial challenging problems in Industry 4.0 can be solved. Deep learning provides algorithms that can give better results on datasets owing to hidden layers. In this chapter, deep learning methods used in Industry 4.0 are examined and explained. In addition, data sets, metrics, methods, and tools used in the previous studies are explained. This study can lead to artificial intelligence studies with high potential to accelerate the implementation of Industry 4.0. Therefore, the authors believe that it will be very useful for researchers and practitioners who want to do research on this topic.
数字工业4.0概念下的深度学习方法研究
如今,工业4.0的主要特征被解释为机器之间以及与系统之间的通信能力,从而提高了生产效率,并开发了机器人的决策机制。在这些情况下,需要新的工业4.0分析算法。通过使用深度学习技术,可以解决工业4.0中的各种工业挑战问题。由于隐藏层的存在,深度学习提供的算法可以在数据集上给出更好的结果。在本章中,对工业4.0中使用的深度学习方法进行了检查和解释。此外,还解释了以往研究中使用的数据集、指标、方法和工具。这项研究可以导致人工智能研究具有很高的潜力,以加速工业4.0的实施。因此,作者认为,对于想要研究这一主题的研究人员和实践者来说,它将非常有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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